Exploiting Spatio-Temporal Coherence for Video Object Detection in Robotics

This paper proposes a method to enhance video object detection for indoor environments in robotics. Concretely, it exploits knowledge about the camera motion between frames to propagate previously detected objects to successive frames. The proposal is rooted in the concepts of planar homography to propose regions of interest where to find objects, and recursive Bayesian filtering to integrate observations over time. The proposal is evaluated on six virtual, indoor environments, accounting for the detection of nine object classes over a total of ∼ 7k frames. Results show that our proposal improves the recall and the F1-score by a factor of 1.41 and 1.27, respectively, as well as it achieves a significant reduction of the object categorization entropy (58.8%) when compared to a two-stage video object detection method used as baseline, at the cost of small time overheads (120 ms) and precision loss (0.92).</p

Proceedings of the 8th Workshop on Detection and Classification of Acoustic Scenes and Events (DCASE 2023)

This volume gathers the papers presented at the Detection and Classification of Acoustic Scenes and Events 2023 Workshop (DCASE2023), Tampere, Finland, during 21–22 September 2023

Multi-modal document processing

Multimodální zpracování dokumentů je oblast informatiky, která se zaměřuje na analýzu, porozumění a získávání cenných informací z dokumentů, které obsahují více typů dat. V této práci je naším hlavním cílem provést analýzu rozložení dokumentů pomocí obrazu i textu. Náš přístup zahrnuje použití modelů pro segmentaci instancí, jako jsou Mask R-CNN, YOLOv8 nebo Cascade R-CNN s páteří LayoutLMv3. Výstupy segmentačních modelů využíváme v multimodálních Transformerech, jako je LayoutLMv3 nebo ve fúzním modelu, který kombinuje německy předtrénovaného BERTa s Vision Transformerem nebo modelem Swin Transformer V2. Dalším přínosem této práce je také nově vytvořená historická datová sada "Heimatkunde", která se skládá z 4 600 anotací na 329 obrázcích a je použitelná pro multimodální analýzu rozložení dokumentů i pro klasifikaci. Naše modely trénujeme na této datové sadě a jsme schopni dosáhnout výborných výsledků. Tyto modely budou proto reálně využity v historickém portálu Porta Fontium.ObhájenoMulti-modal document processing is an area of computer science that focuses on analyzing, understanding, and extracting valuable information from documents that contain multiple types of data. In this work, our main objective is to perform document layout analysis using both visual and textual modalities. Our approach involves the use of instance segmentation models such as Mask R-CNN, YOLOv8, or Cascade R-CNN with a LayoutLMv3 backbone. We employ the outputs of the segmentation models with multi-modal Transformers such as LayoutLMv3 or a fusion model combining German pre-trained BERT with either Vision Transformer or Swin Transformer~V2. Another contribution of this work is a newly created historical "Heimatkunde" dataset, which consists of 4,600 annotations across 329 images and is applicable for multi-modal document layout analysis as well as classification. We train our models on this dataset and are able to achieve excellent results. Therefore, we plan to integrate these models into the Porta Fontium portal

Deep Learning Methods for Remote Sensing

Remote sensing is a field where important physical characteristics of an area are exacted using emitted radiation generally captured by satellite cameras, sensors onboard aerial vehicles, etc. Captured data help researchers develop solutions to sense and detect various characteristics such as forest fires, flooding, changes in urban areas, crop diseases, soil moisture, etc. The recent impressive progress in artificial intelligence (AI) and deep learning has sparked innovations in technologies, algorithms, and approaches and led to results that were unachievable until recently in multiple areas, among them remote sensing. This book consists of sixteen peer-reviewed papers covering new advances in the use of AI for remote sensing

Application and Theory of Multimedia Signal Processing Using Machine Learning or Advanced Methods

This Special Issue is a book composed by collecting documents published through peer review on the research of various advanced technologies related to applications and theories of signal processing for multimedia systems using ML or advanced methods. Multimedia signals include image, video, audio, character recognition and optimization of communication channels for networks. The specific contents included in this book are data hiding, encryption, object detection, image classification, and character recognition. Academics and colleagues who are interested in these topics will find it interesting to read

Advances in Image Processing, Analysis and Recognition Technology

For many decades, researchers have been trying to make computers’ analysis of images as effective as the system of human vision is. For this purpose, many algorithms and systems have previously been created. The whole process covers various stages, including image processing, representation and recognition. The results of this work can be applied to many computer-assisted areas of everyday life. They improve particular activities and provide handy tools, which are sometimes only for entertainment, but quite often, they significantly increase our safety. In fact, the practical implementation of image processing algorithms is particularly wide. Moreover, the rapid growth of computational complexity and computer efficiency has allowed for the development of more sophisticated and effective algorithms and tools. Although significant progress has been made so far, many issues still remain, resulting in the need for the development of novel approaches